{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 解决中文显示问题\n",
    "mpl.rcParams['font.sans-serif'] = [u'SimHei']\n",
    "mpl.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 设置在jupyter中matplotlib的显示情况\n",
    "%matplotlib tk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7dziEHYZy2lnIIHUZVs/y6UcfSPUsr8LqWd5uoofFXOaottPj+2VqdE6I2l1A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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7d2fef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'\n",
    "sizes = [15, 30, 45, 10]\n",
    "explode = (0, 0.1, 0, 0)  # only \"explode\" the 2nd slice (i.e. 'Hogs')\n",
    "\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',\n",
    "        shadow=True, startangle=90)\n",
    "ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
